scipy.stats.sigmaclip

scipy.stats.sigmaclip(a, low=4.0, high=4.0)

Perform iterative sigma-clipping of array elements.

Starting from the full sample, all elements outside the critical range are removed, i.e. all elements of the input array c that satisfy either of the following conditions:

c < mean(c) - std(c)*low
c > mean(c) + std(c)*high

The iteration continues with the updated sample until no elements are outside the (updated) range.

Parameters

aarray_like

Data array, will be raveled if not 1-D.

lowfloat, optional

Lower bound factor of sigma clipping. Default is 4.

highfloat, optional

Upper bound factor of sigma clipping. Default is 4.

Returns

clippedndarray

Input array with clipped elements removed.

lowerfloat

Lower threshold value use for clipping.

upperfloat

Upper threshold value use for clipping.

Examples

>>> from scipy.stats import sigmaclip
>>> a = np.concatenate((np.linspace(9.5, 10.5, 31),
...                     np.linspace(0, 20, 5)))
>>> fact = 1.5
>>> c, low, upp = sigmaclip(a, fact, fact)
>>> c
array([  9.96666667,  10.        ,  10.03333333,  10.        ])
>>> c.var(), c.std()
(0.00055555555555555165, 0.023570226039551501)
>>> low, c.mean() - fact*c.std(), c.min()
(9.9646446609406727, 9.9646446609406727, 9.9666666666666668)
>>> upp, c.mean() + fact*c.std(), c.max()
(10.035355339059327, 10.035355339059327, 10.033333333333333)

>>> a = np.concatenate((np.linspace(9.5, 10.5, 11),
...                     np.linspace(-100, -50, 3)))
>>> c, low, upp = sigmaclip(a, 1.8, 1.8)
>>> (c == np.linspace(9.5, 10.5, 11)).all()
True

scipy.stats.obrientransform scipy.stats.trimboth